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Stochastic robustness synthesis applied to a benchmark control problem
Author(s) -
Marrison Christopher I.,
Stengel Robert F.
Publication year - 1995
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4590050104
Subject(s) - robustness (evolution) , control theory (sociology) , mathematical optimization , monte carlo method , computer science , gaussian , robust control , actuator , quadratic equation , mathematics , engineering , control system , statistics , control (management) , artificial intelligence , biochemistry , chemistry , physics , geometry , quantum mechanics , electrical engineering , gene
Stochastic robustness synthesis is used to find compensators that solve a benchmark problem. The synthesis minimizes a robustness cost function that is the weighted quadratic sum of stochastic robustness metrics. These metrics — probability of instability, probability of actuator saturation, and probability of settling time violation — are estimated using Monte Carlo analysis. A simple search method minimizes the robustness cost by selecting values for the design parameters of a linear quadratic Gaussian regulator. The resulting compensators are robust, require low actuator authority, and compare well with previous designs.